The field of the invention is systems and methods for x-ray mammography. More particularly, the invention relates to systems and methods for estimating the degree of difficulty of finding a lesion within a mammographic image, such as an image acquired with digital mammography, digital tomosynthesis, dual-energy mammography, or contrast-enhanced mammography.
Women presenting with mammographically dense breasts have increased risk of developing breast cancer, and screening mammography has been shown to have reduced sensitivity for dense breasts. While there is considerable interest in quantification of mammographic density, little attention has been directed at developing an index that reflects the “difficulty” of interpreting a mammographic image related to that density.
Mammographic density describes the amount of “fibroglandular tissue,” either on an absolute or relative scale, to the total amount of breast tissue. Fibroglandular tissue is a term used to describe the fibrous (stroma) and glandular (epithelial or parenchymal) tissue components. Other tissues, such as blood vessels, skin, and ligaments also contribute to the radiographic density of breast tissue. The non-dense tissue is largely adipose tissue. In mammography, the fibroglandular (i.e., dense) tissue attenuates x-ray intensities more strongly than adipose tissue, resulting in areas of “density” typically displayed as whiter than the fatty background.
Mammography has reduced accuracy for dense breasts because the lesion has reduced contrast in dense tissue and is thus harder to see. Another reason that mammography has reduced accuracy for dense breasts is because the background tissue structure or “texture” is distracting and thereby causes the lesions to become less conspicuous. This texture is noticeably more apparent in mid- and high-density breasts. Also, mammographic breast density has been identified as an independent risk factor for breast cancer, and studies have identified a 4-5 fold increase in risk for developing breast cancer in women with dense breast tissue versus women with very little dense breast tissue (i.e., breast tissue with more fat), as described by N. F. Boyd, et al., in “Mammographic density and the risk and detection of breast cancer,” N Engl J Med., 2007; 356(3):227-236.
The addition of breast density quantification to mammographic examination has the potential to greatly improve the accuracy of breast cancer risk assessment, especially for those without hereditary or familial risk factors. The inclusion of accurate breast density measurements and masking characteristics can also be potentially helpful for women already determined to be at high risk for breast cancer, for instance, by suggesting that other imaging modalities such as magnetic resonance imaging (“MRI”) or ultrasound be used for initial screening instead of mammography because mammography's accuracy is known to be reduced in women with very dense breasts.
Because the reporting of breast density is required in some jurisdictions, there is a desire to provide an accurate and reproducible quantitative density measurement method that is simple to implement on conventional digital mammography machines. Nevertheless, breast density alone is not a measure of the “difficulty” of a mammographic image. Other factors such as texture, image noise, and x-ray technique factors may affect lesion conspicuity. Lesion “masking” is therefore a result of several parameters that reduce the conspicuity of a lesion. In other words, masking results in the lesion being harder to see in the image.
The current standard of reporting the density of a mammographic image is performed by radiologists in assigning a qualitative Breast Imaging-Reporting and Data System (“BIRADS”) density score (a-d), with “a” being a largely fatty breast, and “d” being a very dense breast. This assessment gives a basic indication of the difficulty of the detection of cancer in that image.
In a recent study, A. E. Burgess, et al. (“Human observer detection experiments with mammographic images and power-law noise,” Med. Phys., 2001; 28(4):419-437), describe a method for using model observers to estimate a measure of lesion masking. In this study, a series of regions-of-interest are extracted from a series of mammographic images, and “simulated” lesions are synthetically added to each ROI. These simulated images are then used in a carefully controlled reader study (called an Alternative-Forced Choice study) to validate the model observers that were used. The study attempted to determine the model observers that best match the humans that participated in the reader study; however, the Burgess study does not otherwise provide a measurement of the difficulty of detecting lesions in a particular mammographic image.